Method for searching related entities through entity co-occurrence

ABSTRACT

A method for searching for related entities using entity co-occurrence is disclosed. Embodiments of the method may be employed in any search system that may include at least one search engine, at least one entity co-occurrence knowledge base, an entity extraction module, and at least an entity indexed corpus. The method may extract and disambiguate entities from search queries by using an entity co-occurrence knowledge base, find extracted entities in an entity indexed corpus and finally present search results as related entities of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 14/557,989, entitled “Method for Searching Related Entities Through Entity Co-Occurrence,” filed on Dec. 2, 2013, which claims a benefit of priority to U.S. Provisional Application 61/910,894, filed Dec. 2, 2013, entitled “Method for Searching Related Entities Through Entity Co-Occurrence,” each of which are incorporated herein by reference in their entirety for all purposes.

This application is related to U.S. patent application Ser. No. 14/557,794, entitled “METHOD FOR DISAMBIGUATED FEATURES IN UNSTRUCTURED TEXT,” filed Dec. 2, 2014, now U.S. Pat. No. 9,239,875 issued Jan. 19, 2016, U.S. patent application Ser. No. 14/558,300, entitled “EVENT DETECTION THROUGH TEXT ANALYSIS USING TRAINED EVENT TEMPLATE MODELS,” filed Dec. 2, 2014, now U.S. Pat. No. 9,177,254 issued Nov. 3, 2015, each of which are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to methods and systems for information retrieval, and more specifically to a method for searching for related entities using entity co-occurrence.

BACKGROUND

In the commercial context, a well known search engine parses a set of search terms and returns a list of items (web pages in a typical search) that are sorted in some manner. Most known approaches, to perform searches, are usually based on historical references of other users to build a search query database that may be eventually used to generate indexes based on keywords. User search queries may include one or more entities identified by name or attributes that may be associated with the entity. Entities may also include organizations, people, location, date and/or time. In a typical search, if a user is searching for information related to two particular organizations, a search engine may return assorted results that may be about a mixture of different entities with the same name or similar names. The latter approach may lead the user to find a very large amount of documents that may not be relevant to what the user is actually interested.

Thus, a need exists for a method for searching for related entities that may grant the user the ability to find related entities of interest.

SUMMARY

A method for searching for related entities using entity co-occurrence is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that may include a client/server type architecture. In one embodiment, the search system may include a user interface for a search engine in communication with one or more server devices over a network connection. The server device may include an entity indexed corpus of electronic data, an entity co-occurrence knowledge base database, and an entity extraction computer module. The knowledge base may be built as an in-memory database and may also include other components such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

In one embodiment, a computer-implemented method comprises receiving, by an entity extraction computer, from a client computer a search query comprising one or more entities; comparing, by the entity extraction computer, each respective entity with one or more co-occurrences of the respective entity in a co-occurrence database; extracting, by the entity extraction computer, a subset of the one or more entities from the search query responsive to determining each respective entity of the subset exceeds a confidence score of the co-occurrence database based on a degree of certainty of co-occurrence of the entity with one or more related entities in an electronic data corpus according to the co-occurrence database; assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities; saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs.

In one embodiment, a system comprising one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including: an entity extraction module configured to receive user input of search query parameters, the entity extraction module being further configured to: extract a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus, assign an index identifier (index ID) to each of the entities in the plurality of extracted entities, save the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; and a search server module configured to search the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur, the search server module being further configured to build a search result list having data records corresponding to the identified index IDs.

In another embodiment, a non-transitory computer readable medium having stored thereon computer executable instructions comprising: receiving, by an entity extraction computer, user input of search query parameters; extracting, by the entity extraction computer, a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus; assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities; saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs.

Definitions

As used here, the following terms may have the following definitions:

“Entity extraction” refers to computer information processing methods for extracting electronic information such as names, places, and organizations.

“Corpus” refers to a collection, such as a computer database, of electronic data, including documents.

“Features” is any information which is at least partially derived from an electronic document.

“Feature attribute” refers to metadata associated with a feature; for example, location of a feature in a document, confidence score, among others.

“Module” refers to a computer hardware and/or software components suitable for carrying out at least one or more tasks.

“Fact” refers to objective relationships between features.

“Entity knowledge base” refers to a computer database containing features/entities.

“Query” refers to a computer generated request to retrieve information from one or more suitable databases.

“Topic” refers to a set of thematic information which is at least partially derived from a corpus.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be better understood by referring to the following figures. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the disclosure. In the figures, reference numerals designate corresponding parts throughout the different views.

FIG. 1 is a block diagram illustrating an exemplary environment of a computer system in which one embodiment of the present disclosure may operate;

FIG. 2 is a flowchart illustrating a method for searching using entity co-occurrence, according to an embodiment; and

FIG. 3 is a flowchart illustrating an embodiment of a simple search where the search results returned by the system may include related entities of interest.

DETAILED DESCRIPTION

The present disclosure is herein described in detail with reference to embodiments illustrated in the drawings, which form a part hereof. Other embodiments may be used and/or other changes may be made without departing from the spirit or scope of the present disclosure. The illustrative embodiments described in the detailed description are not meant to be limiting of the subject matter presented herein.

Reference will now be made to the exemplary embodiments illustrated in the drawings, and specific language will be used here to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended. Alterations and further modifications of the inventive features illustrated here, and additional applications of the principles of the inventions as illustrated here, which would occur to one skilled in the relevant art and having possession of this disclosure, are to be considered within the scope of the invention.

Embodiments of the present disclosure introduce a new search paradigm which grants users the ability to find entities of interest via entity co-occurrence. An important component of this approach is an entity co-occurrence network captured in an entity indexed corpus of electronic data, which is continuously updated as new information is discovered. Moreover, embodiments of the present disclosure incorporate entity extraction and disambiguation, using an entity knowledge base. By exploiting the entity co-occurrence network and disambiguating entities extracted from search queries, the high relevance of search results is ensured so that users obtain precise and direct results containing only the documents with related entities of interest, as discussed in further detail in FIGS. 1-3 below.

FIG. 1 is a block diagram of a search system 100 in accordance with the present disclosure. The search system 100 may include one or more client computing device comprising a processor executing software modules associated with the search system 100, which may include graphical user interfaces 102 accessing a search engine 104 communicating search queries in the form of binary data with a server device 106, over a network 108. In the exemplary embodiment, the search system 100 may be implemented in a client-server computing architecture. However, it should be appreciated that the search system 100 may be implemented using other computer architectures (e.g., a stand-alone computer, a mainframe system with terminals, an application service provider (ASP) model, a peer-to-peer model, and the like). The network 108 may comprise any suitable hardware and software modules capable of communicating digital data between computing devices, such as a local area network, a wide area network, the Internet, a wireless network, a mobile phone network, and the like. As such, it should also be appreciated that the system 100 may be implemented over a single network 108, or using a plurality of networks 108.

A user's computing device 102 may access a search engine 104, which may include software modules capable of transmitting search queries. Search queries are parameters provided to the search engine 104 indicating the desired information to retrieve. Search queries may be provided by a user or another software application in any suitable data format (e.g., integers, strings, complex objects) compatible with the search engine's 104 parsing and processing routines. In some embodiments, the search engine 104 may be a web-based tool that is accessible through the user's computing device 102 browser or other software application, and enables users or software applications to locate information on the World Wide Web. In some embodiments, the search engine 104 may be application software modules native to the system 100, enabling users or applications to locate information within databases of the system 100.

Server device 106, which may be implemented as a single server device 106 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 110, an entity co-occurrence knowledge base 112, and an entity indexed corpus 114. Entity extraction module 110 may be a computer software and/or hardware module able to extract and disambiguate independent entities from a given set of queries such as a query string, structured data and the like. Example of entities may include people, organizations, geographic locations, dates and/or time. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

According to various embodiments, entity co-occurrence knowledge base 112 may be built, but is not limited to, as an in-memory computer database (not shown) and may include other components (not shown), such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation computer module. One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.

Entity co-occurrence knowledge base 112 may include related entities based on features and ranked by a confidence score. Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed. Entity indexed corpus 114 may include data from a plurality of sources such as the Internet having a massive corpus or live corpus.

FIG. 2 is a flowchart illustrating a method 200 for searching related entities using entity co-occurrence that may be implemented in a search system 100, such as the one described in FIG. 1. According to various embodiments, prior to start of method 200, an entity indexed corpus 114 similar to that described by FIG. 1 may have been fed with data from a plurality of sources such as a massive corpus or live corpus of electronic data (e.g., the Internet, website, blog, word-processing file, plaintext file). Entity indexed corpus 114 may include a plurality of indexed entities that may constantly update as new data is discovered.

In one embodiment, method 200 may start when a user or software application of a computing device 102 provides one or more search queries containing one or more entities to a search engine 104, in step 202. Search queries that were provided in step 202 may be processed by search system 100, from one to n, at each time. An example of a search query in step 202 may be a combination of keywords, such as a string, structured data, or other suitable data format. In this exemplary embodiment of FIG. 2, the keywords of the search query may be entities that represent people, organizations, geographic locations, dates and/or times.

Search queries from step 202 may then be processed for entity extraction, in step 204. In this step, the entity extraction module 110 may process search queries from step 202 as entities and compare them all against entity co-occurrence knowledge base 112 to extract and disambiguate as many entities as possible. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

Furthermore, various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed. Once the entities are extracted and ranked based on confidence scores, an index ID, which in some cases may be a number, may be assigned in step 206 to the extracted entities.

Next, in step 208, a search based on the entities index ID assigned in step 206 may be performed. In the search step 208, the extracted entities may be located within the entity indexed corpus 114 data by using standard indexing methods. Once the extracted entities are located, an entity association step 210 may follow. In the entity association step 210, all the data such as documents, videos, pictures, files or the like, where at least two extracted entities overlaps may be pulled from the entity indexed corpus 114. Finally, a list of potential results is built, sorted by relevance, and presented to the user as search results, step 212. The list of results may then show only links to data where the user may find related entities of interest.

FIG. 3 is a particular example of a method 300 for searching related entities using entity co-occurrence, as discussed above in connection with FIG. 2. As described in FIG. 2, according to various embodiments, prior to the start of the method 300, an entity indexed corpus 114 similar to that described by FIG. 1, may have been fed with data from a plurality of sources such as a massive corpus or live corpus (the Internet). Entity indexed corpus 114 may include a plurality of indexed entities that may constantly update as new data is discovered.

In this example embodiment, a user may be looking for information regarding “jobs” at the company “Apple”. For this, the user may input one or more entities (e.g., search queries in step 302) through a user interface 102 which may be, but is not limited to, an interface with a search engine 104, such as the one described in FIG. 1. By a way of illustration and not by way of limitation, the user may input a combination of entities such as “Apple+Jobs”. Next, the search engine 104 may generate search queries, step 302, and send these queries to server device 106 to be processed. At server device 106, entity extraction module 110 may perform an entity extraction step 304 from search queries input in step 302.

Entity extraction module 110 may then process search queries that were input in step 302, such as “Apple” and “Jobs”, as entities and compare them all against entity co-occurrence knowledge base 112 to extract and disambiguate as many entities as possible. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

Furthermore, various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed. As a result, a table 306 including entity and co-occurrences may be created. Table 306 may then show the entity “apple” and its co-occurrences, which in this case, may be Apple and Jobs, Apple and Steve Jobs. The table 306 may also include Apple and organization A which may have been found relevant because Organization A is doing business with Apple and generating “jobs” in said organization A. Other co-occurrences may be found with less importance. As such, Apple and Jobs may then have the highest score (1), thus listed at the top, then Apple and Steve Jobs may have the second highest score (0.8), and finally Apple and other organization A may be at the bottom list with the lowest score (0.3).

Once the entities are extracted and ranked based on confidence scores, an index ID, which in some cases may be a number, may be assigned in step 308 to the extracted entities. Table 310 shows index IDs assigned to extracted entities. Table 310 then shows “Apple” with index ID 1, “Jobs” with index ID 2, “Steve Jobs” with index ID 3, and “Organization A” with index ID 4.

Next, a search step 312 based on the entities index ID 308 may be performed. In the search step 312, the extracted entities such as “Apple”, “Jobs”, “Steve Jobs”, and “Organization A”, may be located within the entity indexed corpus 114 data by using standard indexing methods.

After locating extracted entities within the entity indexed corpus 114, an entity association 314 step may follow. In Entity association step 314, all the data such as documents, videos, pictures, files or the like, where at least two extracted entities overlaps may be pulled from the entity indexed corpus 114 to build a list of links as search results (step 318). By a way of illustration and not by way of limitation, table 316 shows how extracted entities may be associated to data in entity indexed corpus 114. In table 316, documents 1, 4, 5, 7, 8, and 10 show overlapping of two extracted entities, thus the links for these documents may be shown as search results in step 318.

While various aspects and embodiments have been disclosed, other aspects and embodiments are contemplated. The various aspects and embodiments disclosed are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the steps in the foregoing embodiments may be performed in any order. Words such as “then,” “next,” etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Although process flow diagrams may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed here may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Embodiments implemented in computer software may be implemented in software, firmware, middleware, microcode, hardware description languages, or any combination thereof. A code segment or machine-executable instructions may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

The actual software code or specialized control hardware used to implement these systems and methods is not limiting of the invention. Thus, the operation and behavior of the systems and methods were described without reference to the specific software code being understood that software and control hardware can be designed to implement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable or processor-readable storage medium. The steps of a method or algorithm disclosed here may be embodied in a processor-executable software module which may reside on a computer-readable or processor-readable storage medium. A non-transitory computer-readable or processor-readable media includes both computer storage media and tangible storage media that facilitate transfer of a computer program from one place to another. A non-transitory processor-readable storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such non-transitory processor-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other tangible storage medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer or processor. Disk and disc, as used here, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product.

The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined here may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown here but is to be accorded the widest scope consistent with the following claims and the principles and novel features disclosed here. 

What is claimed is:
 1. A computer-implemented method comprising: in response to receiving a search query comprising one or more entities from an end user device, comparing, by the entity extraction computer, each respective entity with one or more co-occurrences of the respective entity in a co-occurrence database; extracting, by the entity extraction computer, a subset of the one or more entities from the search query responsive to determining each respective entity of the subset that exceeds a confidence score of the co-occurrence database based on a degree of certainty of co-occurrence of the entity with one or more related entities in an electronic data corpus according to the co-occurrence database; assigning, by the entity extraction computer, a confidence score to each of the extracted entities, wherein the confidence score is assigned based on a level of certainty that each of the extracted entities is extracted based on one or more feature attributes associated to each of the extracted entities; assigning, by the entity extraction computer, an index identifier (index ID) to each of the extracted entities based at least on the confidence scores of each of the extracted entities, wherein the index ID for each of the extracted entities in saved in the electronic data corpus; identifying, by a search server computer, index IDs of a subset of entities from the extracted entities based on relatedness of index IDs in which at least two of the extracted entities co-occur within the electronic data corpus; and generating, by the search server computer, a search result list having data records corresponding to the identified index IDs.
 2. The computer-implemented method of claim 1, wherein the co-occurrence database comprises one or more entries for the one or more entities, and wherein each entry for the respective entity of the one or more entities contains a semantically-related entity that identifies the respective entity.
 3. The computer-implemented method of claim 1, wherein the co-occurrence is an instance of an entity of the one or more entities identified by the semantically-related entity in the corpus of documents in the co-occurrence database, and wherein the semantically-related entity corresponds to a model indicating distinct entities.
 4. The computer-implemented method of claim 1 further comprising: sorting, by the search server computer, the search result list by relevance based on the confidence score; and transmitting, by the search server computer, the sorted search result list to the end-user device.
 5. The computer-implemented method of claim 1 further comprising: ranking, by the entity extraction computer, the extracted entities based on the confidence score.
 6. The computer-implemented method of claim 1 further comprising: disambiguating, by the entity extraction computer, each of the entities of the extracted entities from one another based on relatedness of index IDs.
 7. The computer-implemented method of claim 1, wherein each of the one or more entities is selected from the group consisting of a person, an organization, a geographic location, a date, and a time.
 8. A system comprising: a co-occurrence database; an entity extraction computer comprising one or more processors executing computer readable instructions for a plurality of computer modules, wherein the entity extraction computer is configured to: in response to receiving a search query comprising one or more entities from an end user device, compare each respective entity with one or more co-occurrences of the respective entity in a co-occurrence database; extract a subset of the one or more entities from the search query responsive to determining each respective entity of the subset that exceeds a confidence score of the co-occurrence database based on a degree of certainty of co-occurrence of the entity with one or more related entities in an electronic data corpus according to the co-occurrence database; assign a confidence score to each of the extracted entities, wherein the confidence score is assigned based on a level of certainty that each of the extracted entities is extracted based on one or more feature attributes associated to each of the extracted entities; and assign an index identifier (index ID) to each of the extracted entities based at least on the confidence scores of each of the extracted entities, wherein the index ID for each of the extracted entities in saved in the electronic data corpus; and a search server computer comprising one or more processors executing computer readable instructions for a plurality of computer modules, wherein the search server computer is configured to: identify index IDs of a subset of entities from the extracted entities based on relatedness of index IDs in which at least two of the extracted entities co-occur within the electronic data corpus; and generate a search result list having data records corresponding to the identified index IDs.
 9. The system of claim 8, wherein the co-occurrence database comprises one or more entries for the one or more entities, and wherein each entry for the respective entity of the one or more entities contains a semantically-related entity that identifies the respective entity.
 10. The system of claim 8, wherein the co-occurrence is an instance of an entity of the one or more entities identified by the semantically-related entity in the corpus of documents in the co-occurrence database, and wherein the semantically-related entity corresponds to a model indicating distinct entities.
 11. The system of claim 8, wherein the entity extraction computer is further configured to: sort the search result list by relevance based on the confidence score and transmit the sorted search result list to the end-user device.
 12. The system of claim 8, wherein the entity extraction computer is further configured to rank the extracted entities based on the confidence score.
 13. The system of claim 8, wherein the entity extraction computer is further configured to disambiguate each of the entities of the extracted entities from one another based on relatedness of index IDs.
 14. The system of claim 8, wherein each of the one or more entities is selected from the group consisting of a person, an organization, a geographic location, a date, and a time.
 15. A non-transitory computer readable medium having stored thereon computer executable instructions comprising: in response to receiving a search query comprising one or more entities from an end user device, comparing, by the entity extraction computer, each respective entity with one or more co-occurrences of the respective entity in a co-occurrence database; extracting, by the entity extraction computer, a subset of the one or more entities from the search query responsive to determining each respective entity of the subset that exceeds a confidence score of the co-occurrence database based on a degree of certainty of co-occurrence of the entity with one or more related entities in an electronic data corpus according to the co-occurrence database; assigning, by the entity extraction computer, a confidence score to each of the extracted entities, wherein the confidence score is assigned based on a level of certainty that each of the extracted entities is extracted based on one or more feature attributes associated to each of the extracted entities; assigning, by the entity extraction computer, an index identifier (index ID) to each of the extracted entities based at least on the confidence scores of each of the extracted entities, wherein the index ID for each of the extracted entities in saved in the electronic data corpus; identifying, by a search server computer, index IDs of a subset of entities from the extracted entities based on relatedness of index IDs in which at least two of the extracted entities co-occur within the electronic data corpus; and generating, by the search server computer, a search result list having data records corresponding to the identified index IDs.
 16. The computer readable medium of claim 15, wherein the co-occurrence database comprises one or more entries for the one or more entities, and wherein each entry for the respective entity of the one or more entities contains a semantically-related entity that identifies the respective entity.
 17. The computer readable medium of claim 15, wherein the co-occurrence is an instance of an entity of the one or more entities identified by the semantically-related entity in the corpus of documents in the co-occurrence database, and wherein the semantically-related entity corresponds to a model indicating distinct entities.
 18. The computer readable medium of claim 15 further comprising: sorting, by the search server computer, the search result list by relevance based on the confidence score; and transmitting, by the search server computer, the sorted search result list to the end-user device.
 19. The computer readable medium of claim 15 further comprising: ranking, by the entity extraction computer, the extracted entities based on the confidence score.
 20. The computer readable medium of claim 15 further comprising: disambiguating, by the entity extraction computer, each of the entities of the extracted entities from one another based on relatedness of index IDs. 